Module #1 Introduction to Cloud-Based AI and Machine Learning Overview of the course, importance of cloud-based AI and ML, and setting up the environment
Module #2 Cloud Computing Fundamentals Introduction to cloud computing, cloud service models, and deployment options
Module #3 AI and ML Overview Introduction to AI and ML, types of AI, and ML workflows
Module #4 Cloud-Based AI and ML Platforms Overview of popular cloud-based AI and ML platforms, such as AWS SageMaker, Google Cloud AI Platform, and Azure Machine Learning
Module #5 Data Ingestion and Preparation Collecting, processing, and preparing data for ML models, including data pipelines and ETL
Module #6 Data Storage and Management Storing and managing large datasets in the cloud, including data lakes and warehouses
Module #7 Machine Learning Algorithms and Models Introduction to popular ML algorithms and models, including supervised, unsupervised, and reinforcement learning
Module #8 Deep Learning Fundamentals Introduction to deep learning, including neural networks, convolutional neural networks, and recurrent neural networks
Module #9 Cloud-Based Deep Learning Building and deploying deep learning models on cloud-based platforms, including TensorFlow and PyTorch
Module #10 Natural Language Processing (NLP) Introduction to NLP, including text preprocessing, tokenization, and language modeling
Module #11 Computer Vision Introduction to computer vision, including image processing, object detection, and segmentation
Module #12 AutoML and Explainability Automating ML workflows using AutoML, and explaining ML models using techniques like LIME and SHAP
Module #13 Cloud-Based Model Deployment Deploying ML models to cloud-based platforms, including containerization and model serving
Module #14 Model Monitoring and Maintenance Monitoring and maintaining ML models in production, including model drift and data quality monitoring
Module #15 Security and Compliance in Cloud-Based AI and ML Ensuring security and compliance in cloud-based AI and ML, including data encryption and access control
Module #16 Scalability and Performance Optimization Optimizing cloud-based AI and ML workflows for scalability and performance, including distributed computing and parallel processing
Module #17 Collaboration and Version Control Collaborating on cloud-based AI and ML projects, including version control using Git and GitHub
Module #18 Cost Optimization and Resource Management Optimizing costs and managing resources in cloud-based AI and ML, including instance selection and cost estimation
Module #19 Specialized AI and ML Services Using specialized AI and ML services, including chatbots, sentiment analysis, and recommender systems
Module #20 Edge AI and IoT Deploying AI and ML models to edge devices, including IoT devices and autonomous systems
Module #21 Explainable AI and Transparency Building transparent and explainable AI models, including model interpretability and fairness
Module #22 Human-in-the-Loop AI Building human-in-the-loop AI systems, including active learning and transfer learning
Module #23 Real-World Applications of Cloud-Based AI and ML Case studies and real-world applications of cloud-based AI and ML, including healthcare, finance, and retail
Module #24 Future of Cloud-Based AI and ML Emerging trends and future directions in cloud-based AI and ML, including quantum AI and Explainable AI
Module #25 Course Wrap-Up & Conclusion Planning next steps in Cloud-Based AI and Machine Learning career
